Retail & E-Commerce Research Agent

Superhighway guides

Retail is a fast-moving, channel-fragmented business where a buying decision, a DTC-vs-marketplace bet, or a markdown plan turns on category share, consumer sentiment, and the trajectory of the digital shelf. This guide builds a Python agent for category managers, e-commerce analysts, retail buyers, DTC brand founders, retail investors, consumer goods manufacturers, and retail technology teams. It chains all four Superhighway endpoints — /research for the category landscape, /search against authoritative retail trade publications and the e-commerce channel ecosystem, /scrape for a specific storefront or article, and /news for same-store sales and promotional activity — then uses an LLM to emit a structured retail brief as JSON.

Overview

The agent takes a brand, category, retailer, or market segment — "Stanley drinkware category tumbler market", "Shein fast fashion ecommerce model competitive analysis" — and produces a structured retail and e-commerce brief:

Synthesizes the category landscape: key players, market structure, competitive dynamics, and historical context
Searches authoritative retail trade publications — Retail Dive, Modern Retail, Chain Store Age, The Robin Report — for category analysis, retailer strategy, consumer trends, store counts, and market share
Searches the e-commerce channel ecosystem — DTC vs. marketplace dynamics, Shopify/Amazon/TikTok Shop, brand aggregators, and consumer behavior data (Comscore, Similarweb, Nielsen)
Scrapes one relevant page: a brand's Shopify storefront, an Amazon category landing page, a Retail Dive article, or a market research excerpt
Pulls recent news: same-store sales results, inventory build/drawdown, promotional activity, store openings/closures, brand launches, and earnings calls
Uses an LLM to generate a structured brief — category overview, competitive landscape, channel dynamics, consumer insights, pricing/promotion, digital shelf, supply chain, and financial signals as JSON

Who it's for: category managers, e-commerce analysts, retail buyers, DTC brand founders, retail investors and VCs, consumer goods manufacturers, and retail technology teams.

How it works

Five endpoint calls feed one LLM synthesis:

/research — deep synthesis of the category landscape: key players, market structure, competitive dynamics, and historical context.
/search (trade publications) — category analysis, retailer strategy, consumer trends, store counts, and market share scoped to Retail Dive, Modern Retail, and Chain Store Age.
/search (e-commerce channel, time=year) — DTC vs. marketplace dynamics, the Shopify/Amazon/TikTok Shop ecosystem, brand aggregators, and consumer behavior data.
/scrape — one relevant URL, e.g. a brand's Shopify storefront, an Amazon category landing page, a Retail Dive article, or a market research excerpt.
/news (time=week) — recent same-store sales, inventory signals, promotional activity, store openings/closures, brand launches, and earnings calls.

Full example

pip install openai requests python-dotenv

Create a .env file with your two keys:

SUPERHIGHWAY_API_KEY=your_key_here
OPENAI_API_KEY=your_key_here

import requests, os, json
from openai import OpenAI

SUPERHIGHWAY_KEY = os.getenv("SUPERHIGHWAY_API_KEY")
BASE = "https://superhighway.walls.sh"
HEADERS = {"Authorization": f"Bearer {SUPERHIGHWAY_KEY}"}

llm = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

# 1. Deep synthesis of the retail / e-commerce category landscape
def research_market(query: str) -> str:
    """Category landscape, key players, market structure, competitive dynamics."""
    r = requests.get(
        f"{BASE}/research",
        params={"q": f"{query} retail ecommerce market category"},
        headers=HEADERS,
    )
    data = r.json()
    return data.get("summary", "")[:3000]

# 2. Authoritative retail trade publications
def search_trade(query: str) -> list[dict]:
    """Category analysis, retailer strategy, consumer trends, store counts, share."""
    r = requests.get(
        f"{BASE}/search",
        params={
            "q": f"{query} site:retaildive.com OR site:modernretail.co "
                 f"OR site:chainstoreage.com retail trends category competition",
        },
        headers=HEADERS,
    )
    return r.json().get("results", [])

# 3. E-commerce channel ecosystem (last year)
def search_ecommerce(query: str) -> list[dict]:
    """DTC vs. marketplace, Shopify/Amazon/TikTok Shop, consumer behavior data."""
    r = requests.get(
        f"{BASE}/search",
        params={
            "q": f"{query} ecommerce DTC marketplace Amazon Shopify brand "
                 f"strategy consumer behavior",
            "time": "year",
        },
        headers=HEADERS,
    )
    return r.json().get("results", [])

# 4. Scrape one relevant storefront / article / market research excerpt
def scrape_page(url: str) -> dict:
    """Pull a Shopify storefront, Amazon category page, Retail Dive article, or report."""
    r = requests.post(
        f"{BASE}/scrape",
        json={"url": url, "mode": "markdown"},
        headers=HEADERS,
    )
    data = r.json()
    return {
        "url": url,
        "title": data.get("title", ""),
        "content": data.get("markdown", data.get("text", ""))[:2500],
    }

# 5. Recent retail news: sales, inventory, promotion (last week)
def get_news(query: str) -> list[dict]:
    """Same-store sales, inventory, promotions, store openings/closures, earnings."""
    r = requests.get(
        f"{BASE}/news",
        params={
            "q": f"{query} retail sales consumer spending inventory promotion discount",
            "time": "week",
        },
        headers=HEADERS,
    )
    return r.json().get("results", [])

def generate_brief(
    query: str,
    market: str,
    trade: list[dict],
    ecommerce: list[dict],
    scraped: dict | None,
    news: list[dict],
) -> dict | None:
    """Generate a structured retail & e-commerce brief as JSON."""

    trade_text = "\n".join(
        f"- {r.get('title', '')}: {r.get('snippet', '')} ({r.get('url', '')})"
        for r in trade[:6]
    )
    ecommerce_text = "\n".join(
        f"- {r.get('title', '')}: {r.get('snippet', '')}"
        for r in ecommerce[:6]
    )
    news_text = "\n".join(
        f"- {n.get('title', '')}: {n.get('snippet', '')}"
        for n in news[:6]
    )
    scraped_text = ""
    if scraped and scraped.get("content"):
        scraped_text = f"{scraped['title']}\n{scraped['content']}"

    response = llm.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "system",
                "content": (
                    "You are a retail and e-commerce analyst. Use ONLY the provided "
                    "sources. Do not invent market shares, revenue figures, price "
                    "points, or company names — if a detail is not in the sources, "
                    "say 'not found in sources.' Be precise about channel mix, "
                    "category economics, and consumer signals, and flag when figures "
                    "may be estimates or sector averages."
                ),
            },
            {
                "role": "user",
                "content": f"""Write a retail & e-commerce brief for: {query}

Category Landscape (synthesis):
{market}

Retail Trade Publications (Retail Dive / Modern Retail / Chain Store Age):
{trade_text}

E-Commerce Channel Ecosystem:
{ecommerce_text}

Scraped Storefront / Article / Market Research Excerpt:
{scraped_text}

Recent Retail News:
{news_text}

Return JSON with ALL of these fields:
- subject: brand, category, retailer, or market segment being researched
- retail_channel: "DTC" | "marketplace" | "wholesale" | "omnichannel" | "specialty-retail" | "grocery" | "convenience" | "B2B" | "mixed"
- category_overview: market size, unit economics, category growth rate, typical margin profile, seasonality patterns
- competitive_landscape: key players, market share estimates, brand positioning, private-label penetration, concentration vs. fragmentation
- channel_dynamics: where is volume sold — DTC website, Amazon, Walmart, Target, TikTok Shop, wholesale; channel mix trends, DTC vs. wholesale tension, margin implications by channel
- consumer_insights: target consumer profile, purchase drivers, brand loyalty vs. price sensitivity, review sentiment trends, NPS proxies, social proof signals
- pricing_and_promotion: price points, discount cadence, promotional strategy (BOGO/% off/bundle), markdown timing, elasticity signals
- digital_shelf: SEO/SEM strategy, Amazon search rank, review count and rating trajectory, social commerce presence (TikTok/Instagram), influencer marketing footprint
- supply_chain_and_inventory: lead times, inventory position signals, stockout risk, sourcing geography, nearshoring activity if applicable
- financial_signals: revenue run rate estimates, fundraising history (DTC brands), comparable public company multiples, PE/VC activity in the category
- regulatory_and_compliance: FTC advertising guidelines, CCPA/privacy for DTC data collection, product safety (CPSC), labeling requirements, tariff exposure
- data_quality: "high" | "medium" | "low" — based on availability of trade publication coverage and public market data""",
            },
        ],
        response_format={"type": "json_object"},
    )

    try:
        return json.loads(response.choices[0].message.content)
    except (json.JSONDecodeError, KeyError):
        return None

def research_retail(query: str) -> dict | None:
    """Run the full retail & e-commerce research pipeline."""
    print(f"Researching retail: {query}")

    print("Synthesizing category landscape...")
    market = research_market(query)

    print("Searching retail trade publications...")
    trade = search_trade(query)

    print("Searching e-commerce channel ecosystem...")
    ecommerce = search_ecommerce(query)

    print("Scraping a relevant storefront / article / report...")
    scraped = None
    for result in trade + ecommerce:
        url = result.get("url")
        if url:
            scraped = scrape_page(url)
            if scraped.get("content"):
                break

    print("Pulling recent retail news...")
    news = get_news(query)

    print("Generating retail brief...")
    return generate_brief(query, market, trade, ecommerce, scraped, news)

def print_brief(brief: dict):
    if not brief:
        print("Could not generate brief.")
        return
    print(f"\n{'='*60}")
    print(f"Retail & E-Commerce Brief")
    print(f"{'='*60}")
    print(f"\nSubject: {brief.get('subject', '')}")
    print(f"Retail Channel: {brief.get('retail_channel', '')}")
    print(f"\nCategory Overview:\n{brief.get('category_overview', '')}")
    print(f"\nCompetitive Landscape:\n{brief.get('competitive_landscape', '')}")
    print(f"\nChannel Dynamics:\n{brief.get('channel_dynamics', '')}")
    print(f"\nConsumer Insights:\n{brief.get('consumer_insights', '')}")
    print(f"\nPricing & Promotion:\n{brief.get('pricing_and_promotion', '')}")
    print(f"\nDigital Shelf:\n{brief.get('digital_shelf', '')}")
    print(f"\nSupply Chain & Inventory:\n{brief.get('supply_chain_and_inventory', '')}")
    print(f"\nFinancial Signals:\n{brief.get('financial_signals', '')}")
    print(f"\nRegulatory & Compliance:\n{brief.get('regulatory_and_compliance', '')}")
    print(f"\nData Quality: {brief.get('data_quality', '?')}")

if __name__ == "__main__":
    import sys
    query = sys.argv[1] if len(sys.argv) > 1 else "Stanley drinkware category tumbler market"
    brief = research_retail(query)
    print_brief(brief)

Usage examples

"Stanley drinkware category tumbler market" — maps the tumbler/drinkware category explosion, positions Stanley vs. YETI vs. Hydro Flask, traces the Amazon vs. DTC channel mix, surfaces the TikTok-driven virality and review trajectory, and flags brand-aggregator activity.
"Shein fast fashion ecommerce model competitive analysis" — profiles ultra-fast-fashion dynamics, compares Shein vs. Zara vs. H&M, covers TikTok Shop integration and DTC channel economics, and surfaces tariff exposure (de minimis rule changes) and regulatory risk (FTC/labor).
"pet food premium DTC subscription category" — covers the premium/fresh pet food category (Farmer's Dog, Nom Nom, Ollie), subscription DTC economics and churn signals, Chewy/Amazon marketplace dynamics, the veterinary channel, and category growth drivers.

Retail market data moves fast — same-store sales, consumer sentiment, and inventory signals shift quarterly. Validate category share and consumer data against primary research sources (NPD/Circana, Nielsen, Numerator, Comscore) and earnings call transcripts for the most current figures.

Getting your API key

Grab a free Superhighway key at /pricing (1,000 calls/month, no credit card). For an agent that provisions its own access, skip the key entirely with x402: it pays $0.002 per call in USDC on Base — no signup, no key management. See the x402 pay-per-call guide for the wallet setup.